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Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems

Miguel Suau, Jinke He, Mustafa Mert Çelikok, Matthijs T. J. Spaan, and Frans A. Oliehoek. Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems. In Advances in Neural Information Processing Systems, pp. 28305–28318, 2022.

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Abstract

Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning. Many real-world problems, however, exhibit overly complex dynamics, making their full-scale simulation computationally slow. In this paper, we show how to factorize large networked systems of many agents into multiple local regions such that we can build separate simulators that run independently and in parallel. To monitor the influence that the different local regions exert on one another, each of these simulators is equipped with a learned model that is periodically trained on real trajectories. Our empirical results reveal that distributing the simulation among different processes not only makes it possible to train large multi-agent systems in just a few hours but also helps mitigate the negative effects of simultaneous learning.

BibTeX Entry

@InProceedings{Suau22neurips,
  author =       {Miguel Suau and Jinke He and Mustafa Mert
                  {\c C}elikok and Matthijs T. J. Spaan and Frans
                  A. Oliehoek},
  title =        {Distributed Influence-Augmented Local Simulators for
                  Parallel {MARL} in Large Networked Systems},
  booktitle =    {Advances in Neural Information Processing Systems},
  pages =        {28305--28318},
  year =         2022
}

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